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Record W1854423876 · doi:10.1089/fpd.2014.1840

Occurrence and Antimicrobial Resistance of <i>Escherichia coli</i> in Oysters and Mussels from Atlantic Canada

2014· article· en· W1854423876 on OpenAlexafffundabout
Erin E. Rees, Jeff Davidson, John M. Fairbrother, S. St. Hilaire, Matthew E. Saab, J. Trenton McClure

Bibliographic record

VenueFoodborne Pathogens and Disease · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Prince Edward Island
FundersCanadian Food Inspection Agency
KeywordsEffluentEscherichia coliContaminationOysterBiologyVirulenceShellfishAntibiotic resistanceHuman healthAntimicrobialVeterinary medicinePathogenic bacteriaMicrobiologyFisheryBacteriaAntibioticsAquatic animalEcologyEnvironmental healthFish <Actinopterygii>Environmental scienceEnvironmental engineeringMedicine

Abstract

fetched live from OpenAlex

Acquiring antimicrobial-resistant (AMR) bacteria through consuming contaminated animal food products is an emerging public health concern, though the sources of contamination are not always clear. This study characterized the occurrence of AMR in Escherichia coli from bivalve molluscs and assessed for the possible sources in the Hillsborough river complex of Prince Edward Island, Canada in areas overlapping with an oyster fishery. Multivariable statistical analysis indicated that the probability of detecting E. coli increased as the estimated dosage of animal effluent contamination decreased. Isolates with AMR were only found from sampling sites closest to untreated human effluent sources. Twenty-seven percent (n=6 of 22) of the isolates were pathogenic, with virulence factors consistent with extraintestinal E. coli of human origin. Though there is more evidence of contamination arising from human effluent, more research is needed to identify driving sources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.205
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2014
Admission routes3
Has abstractyes

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